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109 lines
4.2 KiB
109 lines
4.2 KiB
from typing import Dict, Optional
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import logging
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from celery import Task
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from langchain.agents.agent import AgentExecutor
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from langchain.callbacks.manager import CallbackManager
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from langchain.chains.conversation.memory import ConversationBufferMemory
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from langchain.memory.chat_memory import BaseChatMemory
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from swarms.tools.main import BaseToolSet, ToolsFactory
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from .AgentBuilder import AgentBuilder
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from .Calback import EVALCallbackHandler, ExecutionTracingCallbackHandler
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callback_manager_instance = CallbackManager(EVALCallbackHandler())
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class AgentManager:
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def __init__(self, toolsets: list[BaseToolSet] = []):
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if not isinstance(toolsets, list):
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raise TypeError("Toolsets must be a list")
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self.toolsets: list[BaseToolSet] = toolsets
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self.memories: Dict[str, BaseChatMemory] = {}
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self.executors: Dict[str, AgentExecutor] = {}
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def create_memory(self) -> BaseChatMemory:
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return ConversationBufferMemory(memory_key="chat_history", return_messages=True)
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def get_or_create_memory(self, session: str) -> BaseChatMemory:
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if not isinstance(session, str):
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raise TypeError("Session must be a string")
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if not session:
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raise ValueError("Session is empty")
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if not (session in self.memories):
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self.memories[session] = self.create_memory()
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return self.memories[session]
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def create_executor(self, session_id: str, task: Optional[Task] = None, openai_api_key: str = None) -> AgentExecutor:
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try:
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# Create an agent builder with the provided toolsets
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agent_builder = AgentBuilder(self.toolsets)
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# Setup the parser for the agent
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agent_builder.build_parser()
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# Initialize an empty list for callbacks
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callbacks = []
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# Create and setup an evaluation callback, then add it to the callbacks list
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eval_callback = EvaluationCallbackHandler()
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eval_callback.set_parser(agent_builder.get_parser())
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callbacks.append(eval_callback)
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# If a task is provided, create and setup an execution tracing callback, then add it to the callbacks list
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if task:
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execution_trace_callback = ExecutionTracingCallbackHandler(task)
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execution_trace_callback.set_parser(agent_builder.get_parser())
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callbacks.append(execution_trace_callback)
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# Create a callback manager with the callbacks
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callback_manager = CallbackManager(callbacks)
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# Setup the language model with the callback manager and OpenAI API key
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agent_builder.build_llm(callback_manager, openai_api_key)
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# Setup the global tools for the agent
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agent_builder.build_global_tools()
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# Get or create a memory for the session
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chat_memory = self.get_or_create_memory(session_id)
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# Create a list of tools by combining global tools and per session tools
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tools = [
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*agent_builder.get_global_tools(),
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*ToolsFactory.create_per_session_tools(
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self.toolsets,
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get_session=lambda: (session_id, self.executors[session_id]),
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),
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]
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# Set the callback manager for each tool
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for tool in tools:
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tool.callback_manager = callback_manager
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# Create an executor from the agent and tools
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executor = AgentExecutor.from_agent_and_tools(
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agent=agent_builder.get_agent(),
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tools=tools,
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memory=chat_memory,
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callback_manager=callback_manager,
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verbose=True,
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)
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# Store the executor in the executors dictionary
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self.executors[session_id] = executor
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# Return the executor
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return executor
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except Exception as e:
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logging.error(f"Error while creating executor: {str(e)}")
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raise e
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@staticmethod
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def create(toolsets: list[BaseToolSet]) -> "AgentManager":
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if not isinstance(toolsets, list):
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raise TypeError("Toolsets must be a list")
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return AgentManager(toolsets=toolsets) |